Our solar system now is tied for most number
of planets around a single star, with the recent discovery of an eighth planet
circling Kepler-90, a Sun-like star 2,545 light years from Earth. The planet
was discovered in data from NASA's Kepler Space Telescope.

The newly-discovered Kepler-90i - a sizzling
hot, rocky planet that orbits its star once every 14.4 days - was found using
machine learning from Google. Machine learning is an approach to artificial
intelligence in which computers "learn." In this case, computers learned to
identify planets by finding in Kepler data instances where the telescope
recorded changes in starlight caused by planets beyond our solar system, known
as exoplanets.

Our solar system now is tied for most number of planets around a single star, with the recent discovery of an eighth planet circling Kepler-90, a Sun-like star 2,545 light years from Earth. The planet was discovered in data from NASA's Kepler Space Telescope.

"Just as we expected,
there are exciting discoveries lurking in our archived Kepler data, waiting for
the right tool or technology to unearth them," said Paul Hertz, director of
NASA's Astrophysics Division in Washington. "This finding shows that our data
will be a treasure trove available to innovative
researchers for years to come."

The discovery came about after researchers
Christopher Shallue and Andrew Vanderburg trained a computer to learn how to
identify exoplanets in the light readings recorded by Kepler - the miniscule
change in brightness captured when a planet passed in front of, or transited, a
star. Inspired by the way neurons connect in the human brain, this artificial "neural
network" sifted through Kepler data and found weak transit signals from a
previously-missed eighth planet orbiting Kepler-90, in the constellation Draco.

Machine learning has
previously been used in searches of the Kepler database, and this continuing
research demonstrates that neural networks are a promising tool in finding some
of the weakest signals of distant worlds.

Other planetary systems probably hold more
promise for life than Kepler-90. About 30 percent larger than Earth, Kepler-90i
is so close to its star that its average surface temperature is believed to
exceed 800 degrees Fahrenheit, on par with Mercury. Its outermost planet,
Kepler-90h, orbits at a similar distance to its star as Earth does to the Sun.

"The Kepler-90 star system is like a mini version
of our solar system. You have small planets inside and big planets outside, but
everything is scrunched in much closer," said Vanderburg, a NASA Sagan Postdoctoral Fellow and astronomer
at the University of Texas at Austin.

Shallue, a senior software engineer with
Google's research team Google AI, came up with the idea to apply a neural
network to Kepler data. He became interested in exoplanet discovery after
learning that astronomy, like other branches of science, is rapidly being
inundated with data as the technology for data collection from space advances.

"In my spare time, I started Googling for 'finding
exoplanets with large data sets' and found out about the Kepler mission and the
huge data set available," said Shallue. "Machine learning really shines in
situations where there is so much data that humans can't search it for
themselves."

Kepler's four-year dataset consists of 35,000
possible planetary signals. Automated tests, and sometimes human eyes, are used
to verify the most promising signals in the data. However, the weakest signals
often are missed using these methods. Shallue and Vanderburg thought there
could be more interesting exoplanet discoveries faintly lurking in the data.

First, they trained the neural network to
identify transiting exoplanets using a set of 15,000 previously vetted
signals from the Kepler exoplanet catalogue. In the test set, the neural
network correctly identified true planets and false positives 96 percent of the
time. Then, with the neural network having "learned" to detect the
pattern of a transiting exoplanet, the researchers directed their model to
search for weaker signals in 670 star systems that already had multiple known
planets. Their assumption was that multiple-planet systems would be the best places
to look for more exoplanets.

"We got lots of false positives of planets,
but also potentially more real planets," said Vanderburg. "It's like sifting through rocks to find jewels. If you have a finer sieve
then you will catch more rocks but you might catch more jewels, as well."

Kepler-90i wasn't the only jewel this neural
network sifted out. In the Kepler-80 system, they found a sixth planet. This
one, the Earth-sized Kepler-80g, and four of its neighboring planets form what
is called a resonant chain - where planets are locked by their mutual gravity
in a rhythmic orbital dance. The result is an extremely stable system,
similar to the seven planets in the TRAPPIST-1 system.

Their research paper reporting these findings has been accepted for publication in The
Astronomical Journal. Shallue and Vanderburg plan to apply their neural network
to Kepler's full set of more than 150,000 stars.

Kepler has produced an unprecedented data set
for exoplanet hunting. After gazing at one patch of space for four years, the
spacecraft now is operating on an extended mission and switches its field of
view every 80 days.

"These results demonstrate the enduring value
of Kepler's mission," said Jessie Dotson, Kepler's project scientist at NASA's Ames Research
Center in California's Silicon Valley. "New ways of looking at the data - such as this early-stage research to
apply machine learning algorithms - promise to continue to yield significant
advances in our understanding of planetary systems around other stars. I'm sure
there are more firsts in the data waiting for people to find them."

Ames manages the Kepler and K2
missions for NASA's Science Mission Directorate in Washington. NASA's Jet
Propulsion Laboratory in Pasadena, California, managed Kepler mission
development. Ball Aerospace & Technologies Corporation operates the flight
system with support from the Laboratory for Atmospheric and Space Physics at
the University of Colorado in Boulder. This work was performed through the Carl
Sagan Postdoctoral Fellowship Program executed by the NASA Exoplanet Science
Institute.